Budget Allocation

Budget allocation research focuses on optimizing the distribution of limited resources across competing demands to maximize overall returns, addressing challenges in diverse fields from advertising and marketing to cybersecurity and data markets. Current approaches leverage machine learning, employing techniques like Bayesian hierarchical models, deep reinforcement learning, and causal inference alongside optimization algorithms such as ADMM and integer programming to achieve efficient and effective allocation strategies. These advancements are improving resource utilization in various sectors, leading to enhanced efficiency and profitability, and informing the development of more sophisticated decision-making tools. The field is also exploring novel objective functions and addressing the limitations of existing methods, particularly in handling complex constraints and high-dimensional data.